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【演講宣傳】2020/01/20(一) @工四816,邀請到ECE Department at New York University的Prof. H. Jonathan Chao 演講「CFR-RL
張貼者:曾紫玲/電機工程學系
 
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演講 教職員生 2019/12/26 2020/01/21 工程四館
 

聯絡人:曾紫玲    聯絡電話:54599


演講宣傳2020/01/20() @工四816邀請到ECE Department at New York UniversityProf. H. Jonathan Chao 演講CFR-RL: Traffic Engineering with Reinforcement Learning in SDN

 

IBM中心特別邀請到ECE Department at New York UniversityProf. H. Jonathan Chao前來為我們演講,歡迎有興趣的老師與同學免費報名參加!

 

演講標題:CFR-RL: Traffic Engineering with Reinforcement Learning in SDN

  者:Prof. H. Jonathan Chao (ECE Department at New York University)

      間:2020/01/20() 15:00 ~ 17:00

      點:交大工程四館816

活動報名網址:https://forms.gle/k5txEfTX6jM7PBR98

聯絡方式:曾紫玲  Tel03-5712121分機54599  Emailtzuling@nctu.edu.tw

 

Abstract:

Traffic Engineering (TE) is one of important network features for Software-Defined Networking (SDN) with an aim to help Internet Service Providers (ISPs) optimize network performance and resource utilization by configuring the routing across their backbone networks. Although TE solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwarding the majority of traffic flows using Equal-Cost Multi-Path (ECMP) and selectively rerouting a few critical flows using SDN to balance link utilization of the network. However, critical flow rerouting is not trivial because the solution space for critical flow selection is immense. Moreover, it is impossible to design a heuristic algorithm for this problem based on fixed and simple rules, since rule-based heuristics are unable to adapt to the changes of the traffic matrix and network dynamics. In this talk, we describe a Reinforcement Learning (RL)-based scheme, called CFR-RL, that learns a policy to select critical flows for each given traffic matrix automatically. It then reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming (LP) problem. Extensive evaluations show that CFR-RL outperforms the best heuristic by 7.4% - 12.2% and reroutes only 10% - 21.3% of total traffic.

 

Biography:

H. Jonathan Chao is Professor of Electrical and Computer Engineering (ECE) at NYU, where he joined in January 1992. He is currently Director of High-Speed Networking Lab. He was Head of ECE Department from 2004-2014. He has been doing research in the areas of software defined networking, network function virtualization, datacenter networks, packet processing and switching, network security, and machine learning for networking. He holds 63 patents and has published more than 265 journal and conference papers. During 2000–2001, he was Co-Founder and CTO of Coree Networks, NJ, where he led a team to implement a multi-terabit router with carrier-class reliability. From 1985 to 1992, he was a Member of Technical Staff at Bellcore, where he was involved in network architecture designs and ASIC implementations, such as the world’s first SONET-like Framer chip, ATM Layer chip, Sequencer chip (the first chip handling packet scheduling), and ATM switch chip. He is a Fellow of National Academy of Inventors (NAI) for “having demonstrated a highly prolific spirit of innovation in creating or facilitating outstanding inventions that have made a tangible impact on quality of life, economic development, and the welfare of society.” He is a Fellow of the IEEE for his contributions to the architecture and application of VLSI circuits in high-speed packet networks. He received Bellcore Excellence Award in 1987. He is a co-recipient of the 2001 Best Paper Award from the IEEE Transaction on Circuits and Systems for Video Technology.  He coauthored three networking books. He worked for Telecommunication Lab in Taiwan from 1977 to 1981. He received his B.S. and M.S. degrees in electronics engineering from National Chiao Tung University, Taiwan, in 1977 and 1980, respectively, and his Ph.D. degree in electrical engineering from The Ohio State University in 1985.


附件:海報0120